Week 05 - Assessing Populations at Risk

Wrangle data from DS

In the next exercise, you will find out which kommunes face the problem of having lots of high-risk households, where there is a no car, and they are outside the isochrones and in rural areas (out of the reach of public transport)

Number of households without cars are available via Danmark Statistik via the “Familiernes bilrådighed (faktiske tal) efter rådighedsmønster, område” dataset. The “familier uden biler” sits in csv format as a no_cars.csv with the numbers corresponding to 2020 and 2021 respectively. Overall, there were 1178935 households without a car in 2020 and 1166668 in 2021. The number of course drops with time, while number of car-owning households go up steadily from 1,896,387 in 2020 to 1,929,511 in 2021

• Load the dataset
• rename the V3 and V4 column to nc2020 and nc2021. (In the provided dataset the numeric columns are number of carless households in year 2020 and in 2021

no_cars <- _________("../data/no_cars.csv", header = FALSE)
names___________ <- c("municipality","nc2020", "nc2021") 

It would be nice to know the percentage of households without a car Next, get municipalities of Midtjylland and join up with the no_cars attributes, selecting which kommunes have the least cars and are therefore most at risk.

Percentages of inhabitants without a car per municipality

Create a simple feature or spatial polygons dataset that contains information on the percentage of households that do not have access to a car.

• First read in the type and count of households (“husstande-og-familier”) from 2020 and 2021 from DS . Beware that the dataset has no headers, and read.csv() might perform better than tidyverse counterpart.
• Summarize the hh object after grouping by municipality to get a sum of households per kommune. Rename the resulting columns to municipality and households and label the object hh_mun
• Join the hh_mun to no_cars object by the shared municipality column with inner_join(). Look up the function to know how exactly to specify the shared columns.
• In the resulting hh_nocar object, create two new columns where you calculate the percentage of carless househols for 2020 and 2021, using the total household count and the nc2020 and nc2021 columns.
• Load population data by municipality into pop object in order to compare and contrast with households. How many people on average per household?

– You can either use the provided file (population by municipality from the first quarter of 2022) called munic_pop_2022Q1.csv or, – Concoct your own dataset (you can further break it down by sex or age) from https://www.statbank.dk/FOLK1A

• Load municipality data either via getData() or from a saved object from Week03
• Create no_cars_pct_mun object by joining the households with no car hh_nocar object to the municipalities with inner_join(). Beware to preserve the spatial dimension of the munic for later use in a map. Look up the function to know how exactly to specify the shared columns. You may also want to rename the pop columns first so they are easier to map to munic

# Load tidyverse
library(tidyverse)

# load household data
hh <- read.csv2("../data/households2020_2021.csv", header = FALSE)

# create a summary of households per municipality
hh_mun <- hh %>% 
  group_by(_____) %>% 
  summarize(sum=sum(____)) %>% 
  rename(municipality = V2, households = sum)

# check total households
sum(hh_mun$households)

# join households to no_car dataset and calculate percentages
hh_nocar <- hh_mun %>% inner_join(no_cars, by = "municipality") 
hh_nocar <- hh_nocar %>% 
  mutate(nc2020pct = _______/households*100, 
         nc2021pct = _______/households*100)

# load population (not necessary, I just want to see)
pop <-  read.csv2("../data/munic_pop_2022Q1.csv", sep = ",", skip = 2)

names(pop)[1] <- "Name"
names(pop)[2] <- "Pop22"
sum(pop$Pop22)

# load municipalities
________

# join municipalities with the carless households dataset

no_cars_pct_mun  <-  munic %>% dplyr::inner_join(hh_nocar, by = c("NAME_2"="municipality")) # this preserves the spatial dimension!

[1] 5456264

[1] 11746840

• Create a thematic map of no_cars_pct_mun by filtering municipalities that have 25+ percent of households without a car. I recommend mapview(), but you are free to use your favorite library. Mapview usually works well at the end of tidyverse pipeline but as it may interfere with other libraries, I recommend saving the script and upon restart, running only the mapview() code chunk so as to avoid interference.
• Which areas in Midtjylland have 25 and more percent of carless households?

Now of course, you have the whole situation in Denmark. In the next steps, you need to constrain the municipalities with carless households spatially to those in Midtjylland and check the overlap with hospital isochrones, taking the area outside these isochrones with st_difference().

## YOUR CODE HERE

#region <- getData(country = "DNK", level = 1)
region <- readRDS("../data/gadm36_DNK_1_sp.rds")

# select Midtjylland
Midt <- region %>% 
  as("sf") %>% 
  filter(VARNAME_1 == "Central Jutland")

# intersect with municipalities
no_cars_pct_midt <- no_cars_pct_mun %>% 
  st_simplify(dTolerance = 100) %>% # Let's reduce the weight
 # st_transform(4326) %>% 
  st_intersection(Midt$geometry)

# quick look that we got the right area
library(mapview)

no_cars_pct_midt %>% 
  mapview(zcol = "nc2021pct")

Gosh, that was a ton of wrangling! But we are nearly there! :)

Questions:

3. Are there any areas that are located beyond a 20-minute bike-ride of a hospital that have proportionally lower access to cars? Where are they and what is their total area?

4. How would you ensure residents in these areas have reasonable access to hospitals?

target25pct <- st_union(target_areas)  # gets you area in sq kms
mapview(target25pct)

st_area(target25pct)/1e+06

7817.325 [m^2]